The CamShift is an adaptive version of Mean Shift algorithm. It has received wide attention as an efficient and robust method for object tracking. However, it is often distracted or interfered by the other larger objects with similar colors. This paper presents a novel tracking algorithm based on the mean shift framework. Unlike the CamShift, which uses the probability density image determined by the color feature, the proposed algorithm employs the probability density image derived from both color and shape features. Experimental results indicate the proposed algorithm improves robustness without sacrificing computational cost, as compared to the conventional CamShift algorithm.
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